5-[(α-naphthylmethyl)carbonyl]amino-8-ethyl-2-(2-furyl)-pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine | 1186621-65-7

• 分子结构分类: 有机化合物 - 苯类化合物 - 萘
• 英文名称: 5-[(α-naphthylmethyl)carbonyl]amino-8-ethyl-2-(2-furyl)-pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine
• 英文别名: N-[11-ethyl-4-(furan-2-yl)-3,5,6,8,10,11-hexazatricyclo[7.3.0.02,6]dodeca-1(12),2,4,7,9-pentaen-7-yl]-2-naphthalen-1-ylacetamide
• CAS: 1186621-65-7
• 化学式: C₂₄H₁₉N₇O₂
• 分子量: 437.461
• InChiKey: ZRFQRPBTRKJNBA-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.5
• 重原子数：
  33
• 可旋转键数：
  5
• 环数：
  6.0
• sp3杂化的碳原子比例：
  0.12
• 拓扑面积：
  103
• 氢给体数：
  1
• 氢受体数：
  6

反应信息

• 作为产物：
  描述：

  萘-1-基乙酰氯 、 5-Amino-8-ethyl-2-(2-furyl)-pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine 在 三乙胺 作用下， 以 四氢呋喃 为溶剂， 反应 18.0h， 以90%的产率得到5-[(α-naphthylmethyl)carbonyl]amino-8-ethyl-2-(2-furyl)-pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine
  参考文献：
  名称：

  Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A2A and A3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites

  摘要：

  G Protein-coupled receptors (GPCRs) selectivity is an important aspect of drug discovery process, and distinguishing between related receptor subtypes is often the key to therapeutic success. Nowadays, very few valuable computational tools are available for the prediction of receptor subtypes selectivity.In the present study, we present an alternative application of the Support Vector Machine (SVM) and Support Vector Regression (SVR) methodologies to simultaneously describe both A(2A)R versus A(3)R subtypes selectivity pro. le and the corresponding receptor binding affinities. We have implemented an integrated application of SVM-SVR approach, based on the use of our recently reported autocorrelated molecular descriptors encoding for the Molecular Electrostatic Potential (autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists and to predict their binding affinity to the corresponding receptor subtype of a large dataset of known pyrazolo-triazolo-pyrimidine analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and receptor binding affinity profiles. (C) 2009 Published by Elsevier Ltd.

  DOI：

  10.1016/j.bmc.2009.05.038

文献信息

• Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A2A and A3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites
  作者：Lisa Michielan、Chiara Bolcato、Stephanie Federico、Barbara Cacciari、Magdalena Bacilieri、Karl-Norbert Klotz、Sonja Kachler、Giorgia Pastorin、Riccardo Cardin、Alessandro Sperduti、Giampiero Spalluto、Stefano Moro

  DOI：10.1016/j.bmc.2009.05.038

  日期：2009.7

  G Protein-coupled receptors (GPCRs) selectivity is an important aspect of drug discovery process, and distinguishing between related receptor subtypes is often the key to therapeutic success. Nowadays, very few valuable computational tools are available for the prediction of receptor subtypes selectivity.In the present study, we present an alternative application of the Support Vector Machine (SVM) and Support Vector Regression (SVR) methodologies to simultaneously describe both A(2A)R versus A(3)R subtypes selectivity pro. le and the corresponding receptor binding affinities. We have implemented an integrated application of SVM-SVR approach, based on the use of our recently reported autocorrelated molecular descriptors encoding for the Molecular Electrostatic Potential (autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists and to predict their binding affinity to the corresponding receptor subtype of a large dataset of known pyrazolo-triazolo-pyrimidine analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and receptor binding affinity profiles. (C) 2009 Published by Elsevier Ltd.